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Abstract Volatile threshold resistive switching and neuronal oscillations in phase‐change materials, specifically those undergoing ‘metal‐to‐insulator’ transitions, offer unique attributes such as fast and low‐field volatile switching, tunability, and stochastic dynamics. These characteristics are particularly promising for emulating neuronal behaviors and solving complex computational problems. In this review, we summarize recent advances in the development of volatile resistive switching devices and neuronal oscillators based on three representative materials with coincident electronic and structural phase transitions, at different levels of technological readiness: the well‐studied correlated oxide VO2, the charge‐density‐wave transition metal dichalcogenide 1T‐TaS2, and the emerging phase‐change complex chalcogenide BaTiS3. We discuss progresses from the perspective of materials development and device implementation. Finally, we emphasize the major challenges that must be addressed for practical applications of these phase‐change materials and provides outlook on the future research directions in this rapidly evolving field.
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Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications
Abstract Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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- Award ID(s):
- 2240407
- PAR ID:
- 10441924
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials
- ISSN:
- 0935-9648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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